Computer network controlled data orchestration system and method for data aggregation, normalization, for presentation, analysis and action/decision making

ABSTRACT

Embodiments disclosed include a platform for collecting, normalizing, aggregating, and presenting/processing data over a wide range of devices, machines and applications in real-time, in a wired or wireless networked framework. An embodiment includes a computer automated system and method for aggregating data from a plurality of devices and applications. Embodiments disclosed further include a system and method for normalizing data from a plurality of devices and applications, for canonical-izing all normalized and aggregated data, and via a graphical user interface, combining the aggregated and normalized data, and displaying the combined data in a display compatible format. The computer system is further configured to abstract a plurality of classes of devices via a data modeling language comprised in the configuration of the computer system.

BACKGROUND

Field

This application relates to platforms for collecting, normalizing,aggregating, and presenting/processing data from and to a wide range ofdevices, machines and applications in a wired or wireless networkedframework.

Related Art

As computerization and networked devices capable of communicating witheach other have become all pervasive, this has resulted in a vast amountand number of disparate data sources. Data sources include sensors,devices, mobile devices, machines, applications, etc. Additionally, datasources comprise multiple vendors, varied models of the sensors,devices, mobile devices, machines and applications. Yet additionally,the devices are run by varied/different Operating Systems (OS),protocols etc. Further compounding the problem is a lack ofstandardization, wherein multiple vendors, models, operating systems,protocols, etc. make it impossible for seamless communication acrossdevice models and brands. Current solutions can and have addressed this.Addressing this challenge requires three key steps—1. Data collection,aggregation and normalization, 2. Analysis based on events, conditions,and trends across disparate data sources, and 3. Decision making/Actionsto be taken, often as a return path or closed loop back to themachines/sensors and or other devices/applications.

However, today's systems offer sequential steps that are time consuming,wherein by the time a decision is taken, the data/conditions may havechanged leading to wrong decisions. Further, solutions available areisolated solutions and tend to be silos of a single context, vendor,device type, machine type, application, etc.

There remains a need for automated, real-time, decision making, based onan analysis of conditions across multiple sources. There remains afurther need in such an analysis for a correlation capability across themultiple sources. There also remains an additional need to perform suchanalysis, correlation, and decision making, in real-time, in anautomated fashion. Embodiments disclosed address the above challenges.

SUMMARY

Embodiments disclosed include a platform for collecting, normalizing,aggregating, and presenting/processing data over a wide range ofdevices, machines and applications in real-time, in a wired or wirelessnetworked framework.

An embodiment includes a computer automated system comprising aprocessing unit; a memory element coupled to the processing unit, ameans for communicating over a network, and encoded instructions thatconfigure the computer automated system to aggregate data from aplurality of devices and applications. The configuration further causesthe system to normalize data from a plurality of devices andapplications. The system is also configured to combine the aggregatedand normalized data, and display the combined data in a displaycompatible format. And according to an embodiment, the computer systemis further configured to abstract a plurality of classes of devices viaa data modeling language comprised in the configuration of the computersystem.

An embodiment includes a computer implemented method comprisingaggregating data from a plurality of devices and applications in a dataaggregation server capable of communicating and aggregating the saiddata over a wired or wireless network. The method further includes, in adata normalization engine, normalizing data from a plurality of devicesand applications. And additionally, combining the aggregated andnormalized data, and displaying the combined data in a displaycompatible format through a presentation platform comprising a graphicaluser interface. The method further includes abstracting a plurality ofclasses of devices via a data modeling language wherein the abstractingcomprises abstracting at least one of medical CT scanners, medical MRImachines, printers, and UPS stations.

A mobile wireless communication device configured to, in real time,aggregate data from a plurality of data sources, wherein the saidplurality of data sources comprise a single or plurality of proxydevices, legacy protocols, devices, applications, machines, sensors,things across locations and user types, or device clouds among devicesand applications. The device is further configured to normalize datafrom the said plurality of data sources, and to analyze the aggregated,normalized data based on a correlated event or events, a correlatedcondition or conditions, and a correlated trend or trends across theplurality of data sources. Based on the analyzed data, the mobile devicecombines the aggregated and normalized data, and displays the combineddata in a display compatible format. And according to an embodiment, themobile device is further configured to abstract a plurality of classesof devices via a data modeling language comprised in the configurationof the mobile device, or accessed by the mobile device via a network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the computer system according to an embodiment.

FIG. 2 illustrates data normalization work flow according to anembodiment.

FIG. 3 illustrates the work flow according to an embodiment.

FIG. 4 is a simplified illustration of an embodiment.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. In other instances, well-knownfeatures have not been described in detail to avoid obscuring theinvention.

Embodiments disclosed include a platform for collecting, normalizing,aggregating, and presenting/processing data over a wide range of devicesand applications.

An embodiment includes a computer automated system comprising aprocessing unit, a memory element coupled to the processing unit, and ameans or capability for communicating over a network. FIG. 1 illustratesthe computer system according to an embodiment. The computer automatedsystem includes a data aggregation server 102, a data normalizationengine 104, and a presentation platform comprising a graphical userinterface 106. In the computer system the data aggregation server isconfigured to aggregate data from a plurality of devices 110 andapplications, such as Medical CT scanners 122, Medical MRI machines,124, Printers 126, Universal Power Supply (UPS) devices, 128, etc. Thedata normalization engine 104 is configured to normalize data from thesaid plurality of devices and applications. And the presentationplatform is configured to combine the aggregated and normalized data,and display the combined data in a display compatible format.

According to an embodiment the computer system is further configured toabstract a plurality of classes of devices. This abstracting is done viaa data modeling language comprised in the configuration of the computersystem. The plurality of classes of devices can include medical CTscanners, medical MRI systems, printers, and UPS stations. Thedata-modeling language is used to create an abstract data-model for adevice-class (e.g. Medical CT scanners, Medical MRI systems, Printers,UPS stations, etc.). The data-model contains a list of device-classparameters, their types, whether (say) read-only or read/write, andsynchronous or asynchronous access and propagation, among other things.According to an embodiment, a template (or templates) is/are created toaccess data-model parameters for a particular device model. The templatecontains a mapping from the data-model parameters to one or moreconnectors, specifying the connector name, address/index within theconnector space, and processing instructions to read/write eachparameter in the data-model for the particular device model. Connectorsfor each data-access method such as a network protocol (e.g. SNMPconnector 134, HTTP connector 136), or/and Log File connector 132, whichcan read/write device parameters based on information in the templatefor a particular device model can be used. Parameters for each deviceunder management are either polled periodically or on-demand. Inaddition to periodic or on-demand polling, data can be collected fromdata sources, asynchronously.

Device data is collected using several methods. According to anembodiment, an embedded data collection stack resides on the device. Thedata collections stack interfaces directly with the device using thedevice application programming interface. In an alternate embodiment,the embedded data collection stack resides in a network gateway device.Here the data collections stack interfaces with the concerned deviceover the local area network using a network protocol supported by theconcerned device. In some embodiments, the gateway device may bepre-loaded with the stack by the manufacturer, and proxy software thatresides on and configures a computer in the IP network of the device. Inyet another alternate embodiment, the data collection stack can beinstalled on a computer in the network by the network owner.Alternatively and additionally, it can be installed on an appliance thatis inserted into the network. In the network is also a device cloud thatcan aggregate data for a single or plurality of types of devices, anddevices. Data is collected from the device cloud database using theTCP/IP protocol or any other compatible equivalent protocol. Anembodiment comprises a mobile application, or part of a mobileapplication that resides on and configures a mobile device, such thatthe data collection stack is activated when the mobile applicationexecutes. In some instances, the application can be a background processon the mobile device that runs continuously.

In the computer system, the data normalization engine further comprisesa plurality of extensible connectors (such as Log File Connector 132,SNMP Connector 134, HTTP connector 136), which are used to extract atleast one of log file data, SNMP data and HTTP data from each of aplurality of connected devices. Shown in FIG. 1 are devices 110, whichinclude CT1 111, CT2 112, CT1 113, MRI1 114, MRI2 115, MRI 1 116,Printer1 117, and UPS1 118. Further the system is accordingly configuredto map data-model parameters to variables in device models via apreviously created plurality of templates. The said plurality ofextensible connectors is further configured to extract the data modelparameters mapped to the said variables in the said device models.Additionally, each device parameter is stored as a name value pair in ano-schema database.

According to an embodiment the computer automated system can beconfigured to canonical-ize all normalized and aggregated data. Thiscanonicalization includes simplifying the data by adding Meta data andderived data to device data via a set of workflows. In some embodiments,for example when aggregating data from sources not routed through theConnectors and Normalization path, the workflows also normalize thedata, if needed, before canonicalization. Further, via a plurality ofre-configurable widgets 140, which can include Graph Widget 142, ChartWidget 144, Table Widget 146, Form Widget 148, etc. and via a datamodeling language, the system can display device data in a plurality ofdifferent forms wherein the said plurality of different forms comprisesat least one of a graph, a chart, and a table. Preferably, the saidgraph, chart and table are configured to show data relevant to eachDevice Class.

Presentation across platforms/devices—once the data is aggregated andnormalized, it is stored in a schema-less data store. This allows anytype of data as ‘name-value’ pairs, indexed by a unique identifier foreach device. Access to this data is provided by web services, which areplatform independent and can be consumed by any client application onany platform or device.

Abstraction of a plurality of classes of devices using a data modellinglanguage—A data modelling language is used to capture all relevantinformation about a device class. This abstract data model represents adevice class in general. All concrete manifestations of the device class(different models from different manufacturers) can be mapped onto thedata model of the device class.

The data model representation itself is not specific to a device class.It is a general representation that can be used for any device class. Itcontains the names of the parameters of a device class, their valuetypes, the unique device identifier, variables that can be used toidentify the devices of that class, the protocols it responds to for thedifferent variables both synchronous and asynchronous, and the frequencyof polling for different variables.

FIG. 2 illustrates data normalization according to an embodiment. Datamodel 201 comprises data-modeling language instrumental in creating anabstract data-model for a device-class (e.g. Medical CT, Medical MRI,Printers, and UPS etc.). The data-model contains a list of device-classparameters, their types, whether read-only or read/write, andsynchronous or asynchronous access and propagation, among other things.

Template 202 is created to access data-model parameters for a particulardevice model. The template contains a mapping from the data-modelparameters to one or more connectors, specifying the connector name,address/index within the connector space, and processing instructions toread/write each parameter in the data-model for the particular devicemodel.

Connectors 203 are available for each data-access method such as anetwork protocol (e.g. SNMP, HTTP), which can read/write deviceparameters based on information in the template for a particular devicemodel.

Parameters for each device under management 204 are either polledperiodically or on-demand. In addition to periodic or on-demand polling,data can be collected from data sources, asynchronously.

The abstract data-model now contains <name, value> tuples for eachinstance of a device belonging to device-class. These tuples (names) arenecessarily normalized 205 for all devices belonging to a device-class,based on the definition of the data-model for the device-class.

The normalized data for all devices in a device-class, and acrossdevice-classes, is now aggregated into a schema-less data-base 206 to bestored as <name, value> tuples.

The normalized data stored in the data-base 206 is processed by a set ofdevice-model independent, but device-class specific, workflows 207 toadd metadata and derived data to the extracted device parameter data.

A set of configurable widgets 208 to display device-data and associatedmeta-data/derived-data in different forms (graphs, charts, tables etc.)complete the canonical viewing of the data.

An embodiment includes a computer implemented method comprisingaggregating data from a plurality of devices and applications in a dataaggregation server capable of communicating and aggregating the saiddata over a wired or wireless network. The method further includes, in adata normalization engine, normalizing data from a plurality of devicesand applications. And additionally, combining the aggregated andnormalized data, and displaying the combined data in a displaycompatible format through a presentation platform comprising a graphicaluser interface.

The method further includes abstracting a plurality of classes ofdevices via a data modeling language. The said abstracting comprisesabstracting at least one of medical CT scanners, medical MRI machines,printers, and UPS stations.

FIG. 3 illustrates the work flow according to an embodiment. Step 301includes aggregating data from a plurality of devices and applications.Step 302 triggers normalization of data from the plurality of devicesand applications. Normalization comprises generating an abstractdata-model for a device-class (e.g. Medical CT, Medical MRI, Printers,UPS etc.), as shown in step 302 a. Step 302 b comprises generatingtemplates to access data-model parameters for a particular device model.Step 302 c comprises selection of an access method via a single orplurality of connectors. Step 302 d includes polling of parameters foreach device under management. Step 302 e includes aggregating thenormalized data for all devices in a device-class, and acrossdevice-classes into a schema-less data-base. Step 302 f includes addingmetadata and derived data to the extracted device parameter data. Step303 includes combining the aggregated and normalized data, anddisplaying the combined data in a display compatible format through apresentation platform comprising a graphical user interface. And Step304 includes abstracting the plurality of classes of devices.

In an embodiment, the said normalizing of data includes extracting atleast one of a log file data, an SNMP data and HTTP data from each of aplurality of connected devices via a plurality of extensible connectors.The method further includes mapping data-model parameters to variablesin device models via a plurality of pre-created templates. Andextracting the data model parameters mapped to the said variables in thesaid device models via the said plurality of extensible connectors.

FIG. 4 is a simplified illustration of an embodiment. Preferably thesystem and method are configured to loop through a return path in threesteps or stages: Normalization of data→Analysis andinsights→Decision/Action in real time.

The normalization 401 entails data collection and normalization across avast and disparate amount of data sources. Normalization includesnormalization across proxy devices, legacy protocols, devices, machines,and sensors, things across locations and user types, and device cloudsamong others.

According to an embodiment, if the data is coming through an associatedor recognized proxy, the data is normalized at the collection point orat an edge. According to an alternate embodiment, if the data is comingthrough 3^(rd) party sources or channels then normalization isimplemented at an associated server. Additionally, there could beinstances of the data coming through different sources as well—e.g.Mobile, the associated proxy, through web services etc. In suchinstances, post processing and normalization is done at the associatedserver. Variations, modifications, permutations, and combinations arepossible, as would be apparent to a person having ordinary skill in theart.

The Analysis 402 is based on a correlated event or events, a correlatedcondition or conditions, and a correlated trend or trends across thesedisparate sources of data, analyzed via Device Internet of Things (IOT)stacks and gateways.

This is followed by Decisions/Actions 403 as a return path or closedloop back to the machines/sensors and or other devices/applications.Decisions are taken in real-time based on information obtained from datasources, like the web, social media, etc. and communication devices, inreal-time.

Prior art systems offer sequential steps and the time taken to gothrough these steps are very slow and by the time a decision is taken,the data/conditions may have changed leading to wrong decisions. Also,those that offer such solutions tend to be silos of a single context orvendor or type of device. The decision making is based on analysis ofconditions across multiple sources that need to be correlated and donein real-time. Embodiments disclosed enable this correlation and furtherenable automation of the disclosed systems and methods such thatpreferred embodiments are configured to react in real-time, dependingupon prevailing conditions.

Since various possible embodiments might be made of the above invention,and since various changes might be made in the embodiments above setforth, it is to be understood that all matter herein described or shownin the accompanying drawings is to be interpreted as illustrative andnot to be considered in a limiting sense. Thus it will be understood bythose skilled in the art that although the preferred and alternateembodiments have been shown and described in accordance with the PatentStatutes, the invention is not limited thereto or thereby.

Embodiments disclosed enable seamless communication across device andmachine models, applications, brands, operating systems, networks, andprotocols. Embodiments disclosed further enable real-time datacollection and normalization, real-time analysis based on events,conditions, and trends across disparate data sources, and real-timedecision making/action as a return path or closed loop back to themachines/sensors and or other devices/applications. Embodimentsdisclosed also enable the return path or the control path forconfiguration, control and diagnostics. Preferred embodiments include acorrelation capability across the multiple sources in real-time analysisand decision making. Embodiments disclosed also enable working acrossmultiple silos.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems and methods according to variousembodiments of the present invention. It should also be noted that, insome alternative implementations, the functions noted/illustrated mayoccur out of the order noted in the figures. For example, two blocksshown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

In general, the steps executed to implement the embodiments of theinvention, may be part of an automated or manual embodiment, andprogrammable to follow a sequence of desirable instructions.

The present invention and some of its advantages have been described indetail for some embodiments. It should be understood that although someexample embodiments of the data aggregation, normalization andpresentation system and method are described with reference to specificdevice types and applications, the computer implemented system andmethod is highly reconfigurable, and embodiments include reconfigurablesystems that may be dynamically adapted to be used in other contexts aswell. It should also be understood that various changes, substitutionsand alterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims. An embodimentof the invention may achieve multiple objectives, but not everyembodiment falling within the scope of the attached claims will achieveevery objective. Moreover, the scope of the present application is notintended to be limited to the particular embodiments of the process,machine, manufacture, and composition of matter, means, methods andsteps described in the specification. A person having ordinary skill inthe art will readily appreciate from the disclosure of the presentinvention that processes, machines, manufacture, compositions of matter,means, methods, or steps, presently existing or later to be developedare equivalent to, and fall within the scope of, what is claimed.Accordingly, the appended claims are intended to include within theirscope such processes, machines, manufacture, compositions of matter,means, methods, or steps

We claim:
 1. A computer automated system comprising: a processing unit;a memory element coupled to the processing unit; an embedded datacollection stack; wherein the computer automated system is configuredto, in real-time: automatically aggregate device behavior data over anetwork via the embedded data collection stack from a plurality ofdevice classes, wherein the plurality of device classes comprise asingle or plurality of proxy devices, legacy protocols, devices,applications, machines, and sensors across locations; abstract theplurality of device classes to generate an abstract device model;automatically normalize the aggregated device behavior data from theplurality of device classes; wherein automatic normalization comprisesnormalization at a collection point or at an edge from an associated orrecognized proxy, and normalization at an associated server from anunassociated or unrecognized proxy; automatically canonicalize thenormalized and aggregated device behavior data, wherein canonicalizationcomprises adding meta-data and derived data to aggregated devicebehavior data from each of the plurality of device classes;automatically analyze the canonicalize device behavior data based on acorrelated event or events, and a correlated condition or conditions,across the plurality of device classes; and based on the analyzed devicebehavior data, automatically combine the normalized and aggregateddevice behavior data, and display the combined normalized and aggregateddevice behavior data in a display compatible format.
 2. The computerautomated system of claim 1 wherein the abstracting of the plurality ofdevice classes comprises abstracting a plurality of sensors, andconnected devices comprising medical CT scanners, medical Mills,printers, and UPS systems.
 3. The computer automated system of claim 1wherein, in the normalizing of said device behavior data from theplurality of device classes, the computer automated system is furthercaused to: via a plurality of extensible connectors, extract a log filedata, or a proprietary or standard protocol comprising SNMP data andHTTP data from each of a plurality of connected devices.
 4. The computerautomated system of claim 3 wherein the system is further configured to:map device behavior data model parameters to variables in device modelsvia a previously created plurality of templates; and wherein theplurality of extensible connectors are further caused to extract thedevice behavior data model parameters mapped to the variables in thedevice models.
 5. The computer automated system of claim 4 wherein thecomputer automated system is configured to: store each device behaviordata model parameter as a name value pair in a no-schema database. 6.The computer automated system of claim 1 wherein: said adding meta-dataand derived data to the aggregated device behavior data from each of theplurality of device classes in canonicalization of the normalized andaggregated device behavior data comprises adding the meta-data and thederived data via a single or plurality of workflows.
 7. The computerautomated system of claim 1 wherein the computer automated system isconfigured to: display the device behavior data in a plurality ofdifferent forms wherein the plurality of different forms comprises atleast one of a graph, a chart, and a table.
 8. The computer automatedsystem of claim 7 wherein the graph, chart and table are configured toshow the device behavior data relevant to each device class.
 9. Thecomputer automated system of claim 1 wherein the analysis based on thecorrelated event or events, and the correlated condition or conditions,across the plurality of device classes, comprises analysis via a singleor plurality of Device Internet of Things (IOT) stacks and gateways; andwherein based on the analysis, the computer automated system isconfigured to implement a single or plurality of decisions, inreal-time, in a return path or closed loop, on a plurality of machines,sensors, devices and applications.
 10. The computer automated system ofclaim 1 further comprising a mobile device.
 11. In a computer automatedsystem comprising a processing unit coupled to a memory element, anembedded data collection stack, and having instructions encoded thereon,a method comprising, in real-time: automatically aggregating devicebehavior data over a network via the embedded data collection stack froma plurality of device classes, wherein the plurality of device classescomprise a single or plurality of proxy devices, legacy protocols,devices, applications, machines, sensors and things across locations;abstracting the plurality of device classes to generate an abstractdevice model; automatically normalizing the aggregated device behaviordata from the plurality of device classes; wherein automaticnormalization comprises normalization at a collection point or at anedge from an associated or recognized proxy, and normalization at anassociated server from an unassociated or unrecognized proxy;automatically canonicalizing the normalized and aggregated devicebehavior data, wherein canonicalization comprises adding meta-data andderived data to aggregated device behavior data from each of theplurality of device classes; automatically analyzing the canonicalizeddevice behavior data based on a correlated event or events, a correlatedcondition or conditions, and a correlated trend or trends across theplurality of device classes; and based on the analyzed device behaviordata, automatically combining the normalized and aggregated devicebehavior data, and displaying the combined normalized and aggregateddevice behavior data in a display compatible format.
 12. The method ofclaim 11 wherein the abstracting of the plurality of classes of theplurality of device classes comprises abstracting a plurality ofsensors, and connected devices comprising medical CT scanners, medicalMills, printers, and UPS systems.
 13. The method of claim 11 wherein thenormalizing of aggregated device behavior data further comprises:extracting a log file data, or a proprietary or standard protocolcomprising at least one of an SNMP data and HTTP data from each of aplurality of connected devices via a plurality of extensible connectors.14. The method of claim 13 further comprising: mapping device behaivordata model parameters to variables in device class models via aplurality of pre-created templates; and extracting the device behaviordata model parameters mapped to the variables in the device class modelsvia the plurality of extensible connectors.
 15. The method of claim 14further comprising: storing each device behaivor data model parameter asa name value pair in a no-schema database.
 16. The method of claim 11wherein: said adding meta-data and derived data to the aggregated devicebehavior data from each of the plurality of device classes incanonicalization of the normalized and aggregated device behavior datacomprises adding the meta-data and the derived data via a single orplurality of workflows.
 17. The method of claim 11 further comprising:displaying device behavior data in a plurality of forms wherein the saidplurality of forms comprises at least one of a graph, a chart, and atable.
 18. The method of claim 17 wherein the said graph, chart andtable are configured to show behavior data relevant to each deviceclass.
 19. The method of claim 11 wherein the analyzing of devicebehavior data based on the correlated event or events, and thecorrelated condition or conditions, across the plurality of deviceclasses comprises: analyzing via a single or plurality of DeviceInternet of Things (IOT) stacks and gateways; and based on the analyzingvia the single or plurality of Device Internet of Things (IOT) stacksand gateways, implementing a single or plurality of actions, inreal-time, in a return path or closed loop, on a single or plurality ofmachines, sensors, devices or applications.
 20. In a computer automatedsystem comprising a processing unit coupled to a memory element andhaving instructions encoded thereon, a method comprising, automaticallyin real-time: via an embedded data collection stack comprised in thecomputer automated system, aggregating device behavior data over anetwork from a plurality of device classes, wherein the plurality ofdevice classes comprise a single or plurality of proxy devices, legacyprotocols, devices, applications, machines, sensors and things acrosslocations; normalizing the aggregated device behavior data from theplurality of device classes, wherein the normalizing comprises:generating an abstract device model for the plurality of device classes;extracting device model parameters via the generated abstract devicemodel; polling the extracted device model parameters for each deviceclass type from the plurality of device classes; wherein the normalizingof the aggregated device behavior data from the plurality of deviceclasses further comprises normalization at a collection point or at anedge from an associated or recognized proxy, and normalization at anassociated server from an unassociated or unrecognized proxy;canonicalizing the normalized and aggregated device behavior data whichcomprises adding meta-data and derived data to the extracted devicemodel parameters; analyzing the canonicalized device behavior data basedon a correlated event or events, and a correlated condition orconditions, across the plurality of device classes; and based on theanalyzing: implementing a single or plurality of actions, in real-time,in a return path or closed loop, on a single or plurality of machines,sensors, devices or applications; and combining the normalized andaggregated device behavior data, and displaying the combined normalizedand aggregated device behavior data in a display compatible format. 21.A mobile wireless communication device comprising: a processing unit; amemory element coupled to the processing unit; an embedded datacollection stack; encoded instructions that configure the mobile deviceto, automatically in real-time: aggregate device behavior data over anetwork via the embedded data collection stack from a plurality ofdevice classes, wherein the plurality of device classes comprise asingle or plurality of proxy devices, legacy protocols, devices,applications, machines, sensors and things across locations; abstractthe plurality of device classes to generate an abstract device model;normalize the aggregated device behavior data from the plurality ofdevice classes; wherein automatic normalization comprises normalizationat a collection point or at an edge from an associated or recognizedproxy, and normalization at an associated server from an unassociated orunrecognized proxy; canonicalize the normalized and aggregated devicebehavior data, wherein canonicalization comprises adding meta-data andderived data to aggregated device behavior data from each of theplurality of device classes; analyze the canonicalized device behaviordata based on a correlated event or events, a correlated condition orconditions, and a correlated trend or trends across the plurality ofdevice classes; and based on the analyzed device behavior data, combinethe normalized and aggregated device behavior data, and display thecombined normalized and aggregated device behavior data in a displaycompatible format.
 22. The mobile wireless communication device of claim21 wherein the device is further configured to: based on the analyzeddevice behavior data, trigger a single or plurality of actions, inreal-time, in a return path or closed loop, on the single or pluralityof machines, sensors, devices or applications.